https://openalex.org/T12794
This cluster of papers focuses on the application of Adaptive Dynamic Programming and Reinforcement Learning techniques to solve optimal control problems in continuous-time nonlinear systems. It explores the use of neural networks, policy iteration, actor-critic algorithms, and $H_{infty}$ control for online learning and feedback control in various domains such as robotics, energy management, and multi-agent systems.
@prefix oasubfields: <https://openalex.org/subfields/> .
@prefix openalex: <https://lambdamusic.github.io/openalex-hacks/ontology/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
<https://openalex.org/T12794> a skos:Concept ;
rdfs:label "Adaptive Dynamic Programming for Optimal Control"@en ;
rdfs:isDefinedBy openalex: ;
owl:sameAs <https://en.wikipedia.org/wiki/Adaptive_dynamic_programming>,
<https://openalex.org/T12794> ;
skos:broader oasubfields:1703 ;
skos:definition "This cluster of papers focuses on the application of Adaptive Dynamic Programming and Reinforcement Learning techniques to solve optimal control problems in continuous-time nonlinear systems. It explores the use of neural networks, policy iteration, actor-critic algorithms, and $H_{infty}$ control for online learning and feedback control in various domains such as robotics, energy management, and multi-agent systems."@en ;
skos:inScheme openalex: ;
skos:prefLabel "Adaptive Dynamic Programming for Optimal Control"@en ;
openalex:cited_by_count 136937 ;
openalex:works_count 6820 .